An Empirical Relationship Between Coronal Density and Solar Wind Velocity in the Middle Corona With Applications to Space Weather

An Empirical Relationship Between Coronal Density and Solar Wind Velocity in the Middle Corona With Applications to Space Weather
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中日冕中日冕密度与太阳风速之间的经验关系及其在空间天气中的应用

DOI:
10.1029/2023sw003448
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发表时间:
2023
期刊:
影响因子:
3.7
通讯作者:
Bunting K
Bunting K
中科院分区:
地球科学1区
文献类型:
--
作者:
Bunting K

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对周围太阳风状况的准确预测是空间气象预报的一个核心组成部分。最近的一个进展是使用在日心距离为8 R的电子密度分布,通过将日冕旋转层析成像应用于日冕数据获得,作为时间依赖的日光层逆风外推太阳风模型的内边界条件。这种方法需要将密度转换为内边界处的太阳风速度。基于现场测量的密度和速度的分布的比较,这项工作发现了一个比例指数方程的密度和流出速度在8 R,与三个关键参数发现作为一个函数的时间之间的2007年至2021年。基于这种关系,在过去的太阳活动周期中,地球、STEREO A和STEREO B的模拟和现场速度测量值的比较给出的平均绝对误差分别为61.2、69.0和66.1 km s-1。对数千个事件(定义为450 km s−1以上的太阳风流)的分析给出了76%的准确度。这一一致性验证了密度-速度关系,并表明基于日冕观测的内边界是对太阳风建模和预测常用磁模型约束的一种强大补充或替代。
Accurate predictions of ambient solar wind conditions are a central component of space weather forecasting. A recent advancement is to use the distribution of electron density at a heliocentric distance of 8 R⊙, gained by applying coronal rotational tomography to coronagraph data, as an inner boundary condition for the time‐dependent Heliospheric Upwind eXtrapolation solar wind model. This approach requires conversion of densities into solar wind velocity at the inner boundary. Based on comparison of the distribution of in situ measurements of density and velocities, this work finds a scaled exponential equation relating the density and outflow velocity at 8 R⊙, with three key parameters found as a function of time between years 2007–2021. Based on this relationship, comparison of modeled and in situ measurements of velocities at Earth, STEREO A and STEREO B over the past solar cycle give a mean absolute error of 61.2, 69.0, and 66.1 km s−1respectively. An analysis of thousands of events (defined as solar wind streams above 450 km s−1) gives an accuracy score of 76%. This agreement validates the density‐velocity relationship, and shows that an inner boundary based on coronagraph observations is a robust complement, or alternative, to commonly‐used magnetic model constraints for solar wind modeling and forecasting.
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